Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

Zhang, Xiangsheng; Pan, Feng

doi:10.1155/2015/320130

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The authors also define the theoretical description of the performance advantages. In [56] authors introduced the improved version of the PSO algorithm (IPSO) to the selection of optimal parameters for the mixed kernel function of the SVM model. In [57], studied the development of a soft-sensing model based on LS-SVM to estimate the unmeasurable variables in industrial procedures.…”

Section: Support Vector Machine-based Soft-sensing Modelsmentioning

confidence: 99%

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Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

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Section: Support Vector Machine-based Soft-sensing Modelsmentioning

confidence: 99%

“…In Zhu and Zhu [50], authors utilized the PSO algorithm with GPC-LS-SVM soft-sensing model. In another research paper, the improved version of PSO was utilized by [56], just given a few examples.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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“…A support vector machine model can be used as a regression prediction model in various fields with effective adaptation for small-sample problems. − However, due to the existence of a single support vector machine, the model accuracy is strongly dependent on the hyperparameters, and the model has no global optimization ability in the hyperparameter space, so the model fitting results are usually unstable . In recent years, many scholars have improved the inherent defects of single support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Coal Spontaneous Combustion Based on SA-SVM

et al. 2021

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Accurate predictions of the coal temperature in coal spontaneous combustion (CSC) are important for ensuring coal mine safety. Gas coal (the Zhaolou coal mine in Shandong Province, China) was used in this paper. A large CSC experimental device was adopted to obtain its characteristic temperatures from the macroscopic characteristics of gas production. A simulated annealing-support vector machine (SA-SVM) prediction model was proposed to reflect the complex nonlinear mapping between characteristic gases and the coal temperature. The risk degree of CSC was estimated in the time domain, and the model was verified by using in situ data from an actual working face. Furthermore, back-propagation neural network (BPNN) and single SVM methods were adopted for comparison. The results showed that the BPNN could not adapt to the small-sample problem due to overfitting and the output of a single SVM was unstable due to its strong dependence on the setting of hyperparameters. Through the SA global optimization process, the optimal combination of hyperparameters was obtained. Therefore, SA-SVM had higher prediction accuracy, robustness, and error tolerance rate and better environmental adaptability. These findings have certain practical significances for eliminating the hidden danger of CSC in the gob and providing timely warnings about potential danger.

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“…e least-squares' ground projection method of the double support-vector machine reduces the diagnostic error in [28]. Meanwhile, to optimize the penalty factor C and kernel parameter of LSSVM, some new algorithms such as the Moth-flame Optimization (MFO), the von Neumann Topology Whale Optimization Algorithm (VNWOA), Quantum Particle Swarm (QPS), and Chaotic Antlion Algorithm (CAA) were introduced to implement the optimization operation for enhancing the precision of fault diagnosis in [29][30][31][32][33][34]. e experiments have verified the performance of these presented algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Huang

Qin

2021

Mathematical Problems in Engineering

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To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

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Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

Cited by 4 publications

References 15 publications

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Prediction Model for Coal Spontaneous Combustion Based on SA-SVM

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

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